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Record W2894919975 · doi:10.2166/wst.2018.404

Mineralization of quinoline in aqueous solution by microwave-assisted catalytic wet peroxide oxidation system: process optimization, products analysis and degradation route research

2018· article· en· W2894919975 on OpenAlexaff
Zhipeng Li, Feng Liu, Bo Zhang, Yi Ding, Hong You, Chao Jin

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Urban Water Resource and EnvironmentNatural Science Foundation of Shandong ProvinceHarbin Institute of TechnologyChina Postdoctoral Science Foundation
KeywordsMineralization (soil science)QuinolineMicrowaveAqueous solutionChemistryCatalysisPeroxideDegradation (telecommunications)Mass spectrometryNuclear chemistryChromatographyChemical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The experimental design methodology was used to optimize the experimental parameters of quinoline mineralization by microwave-enhanced catalytic wet peroxide oxidation (CWPO). Initial pH value, temperature, H2O2 dosage, and microwave power were selected as independent variables. The mineralization efficiency approached 83.82% under the optimized conditions: initial pH 6.00, temperature 60 °C, H2O2 dosage 0.09 mol/L, and microwave power 565.10 W. Regression analysis with an R2 value of 0.9867 showed a good agreement between the experimental results and the predicted values. Furthermore, based on the detection and identification of products by gas chromatography mass spectrometry, the oxidation degradation pathways of quinoline were proposed. The energy balance and costs analysis indicated that the total cost of the microwave-enhanced CWPO process for wastewater treatment was 40.60 yuan/m3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.281
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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